In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i.e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence. The existing approaches to RefVSR essentially attempt to align the reference and the input sequence, in the presence of resolution gap and long temporal range. However, they either ignore temporal structure within the input sequence, or suffer accumulative alignment errors. To address these issues, we propose EFENet to exploit simultaneously the visual cues contained in the HR reference and the temporal information contained in the LR sequence. EFENet first globally estimates cross-scale flow between the reference and each LR frame. Then our novel flow refinement module of EFENet refines the flow regarding the furthest frame using all the estimated flows, which leverages the global temporal information within the sequence and therefore effectively reduces the alignment errors. We provide comprehensive evaluations to validate the strengths of our approach, and to demonstrate that the proposed framework outperforms the state-of-the-art methods. Code is available at https://github.com/IndigoPurple/EFENet.

翻译：在本文中,我们考虑了基于参考的视频超级分辨率(RefVSR)问题,即如何利用高分辨率参考框架(HR)超级解析低分辨率(LR)视频序列。现有RfVSR方法基本上试图在分辨率差距和较长时间范围内使参考和输入序列保持一致。然而,这些方法要么忽视输入序列中的时间结构,要么造成累积校正错误。为解决这些问题,我们提议EFENet同时利用HR参考和LR序列中的时间信息中的视觉提示。EFENet首先对全球范围参照和每个LR框架之间的跨比例流动进行全球估计。然后,EFENet的新流程改进模块利用所有估计流,利用序列中的全球时间信息,改进最远框架的流程,从而有效减少校正错误。我们提供了全面评价,以验证我们的方法的优点,并表明拟议的框架超越了最新技术方法。https://giuburst/Incomformation,可在https://giuthurNet/EEEFO.code查阅。